Generalize dtype check in `python_to_sql_type`

[charlesbluca]

Chatting with @DaceT exposed that python_to_sql_type only has handling for pandas / numpy types, which can cause issues when attempting to read in a table with a cuDF dtype, like so:

import cudf

from dask_sql import Context

c = Context()
cdf = cudf.datasets.timeseries()

c.create_table("tble", cdf)
result = c.sql("""SELECT timestamp FROM cdf""") 
result.compute()

TypeError: unhashable type: 'CategoricalDtype'

A pretty simple workaround for this is generalizing the column dtype check so that instead of checking for a np.dtype, we check if the Python type is "dtype-like".

Interested in if there's a trivial util function like is_dataframe_like for dtypes?

[codecov-commenter]

Codecov Report

Merging #402 (33a1801) into main (00df420) will increase coverage by 0.14%. The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main     #402      +/-   ##
==========================================
+ Coverage   88.94%   89.09%   +0.14%     
==========================================
  Files          68       68              
  Lines        3337     3337              
  Branches      657      657              
==========================================
+ Hits         2968     2973       +5     
+ Misses        297      288       -9     
- Partials       72       76       +4     

Impacted Files	Coverage Δ
dask_sql/mappings.py	100.00% <100.00%> (ø)
dask_sql/_version.py	34.00% <0.00%> (+1.44%)	:arrow_up:

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[charlesbluca]

Things are a little more complicated than I initially assumed here - opened #423 to track this issue